Prompting in the Wild: An Empirical Study of Prompt Evolution in Software Repositories
Fuente:
arXiv
Saved in:
| Main Authors: | Tafreshipour, Mahan, Imani, Aaron, Huang, Eric, Almeida, Eduardo, Zimmermann, Thomas, Ahmed, Iftekhar |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?
by: Suh, Hyunjae, et al.
Published: (2024)
by: Suh, Hyunjae, et al.
Published: (2024)
Human or LLM? A Comparative Study on Accessible Code Generation Capability
by: Suh, Hyunjae, et al.
Published: (2025)
by: Suh, Hyunjae, et al.
Published: (2025)
Does Documentation Matter? An Empirical Study of Practitioners' Perspective on Open-Source Software Adoption
by: Imani, Aaron, et al.
Published: (2024)
by: Imani, Aaron, et al.
Published: (2024)
Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks
by: Santana Jr, E. G., et al.
Published: (2025)
by: Santana Jr, E. G., et al.
Published: (2025)
Context Conquers Parameters: Outperforming Proprietary LLM in Commit Message Generation
by: Imani, Aaron, et al.
Published: (2024)
by: Imani, Aaron, et al.
Published: (2024)
Inside Out: Uncovering How Comment Internalization Steers LLMs for Better or Worse
by: Imani, Aaron, et al.
Published: (2025)
by: Imani, Aaron, et al.
Published: (2025)
Investigating the Impact of Code Comment Inconsistency on Bug Introducing
by: Radmanesh, Shiva, et al.
Published: (2024)
by: Radmanesh, Shiva, et al.
Published: (2024)
From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models
by: Chen, QiHong, et al.
Published: (2025)
by: Chen, QiHong, et al.
Published: (2025)
What Makes a Great Software Quality Assurance Engineer?
by: Farias, Roselane Silva, et al.
Published: (2024)
by: Farias, Roselane Silva, et al.
Published: (2024)
Test Smell: A Parasitic Energy Consumer in Software Testing
by: Misu, Md Rakib Hossain, et al.
Published: (2023)
by: Misu, Md Rakib Hossain, et al.
Published: (2023)
A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering
by: de Martino, Vincenzo, et al.
Published: (2024)
by: de Martino, Vincenzo, et al.
Published: (2024)
Analyzing the Evolution and Maintenance of Quantum Software Repositories
by: Upadhyay, Krishna, et al.
Published: (2025)
by: Upadhyay, Krishna, et al.
Published: (2025)
Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study
by: Santana Jr, E. G., et al.
Published: (2025)
by: Santana Jr, E. G., et al.
Published: (2025)
Is Multi-Agent Debate (MAD) the Silver Bullet? An Empirical Analysis of MAD in Code Summarization and Translation
by: Chun, Jina, et al.
Published: (2025)
by: Chun, Jina, et al.
Published: (2025)
From Prompt-Response to Goal-Directed Systems: The Evolution of Agentic AI Software Architecture
by: Alenezi, Mamdouh
Published: (2026)
by: Alenezi, Mamdouh
Published: (2026)
Green Prompt Engineering: Investigating the Energy Impact of Prompt Design in Software Engineering
by: De Martino, Vincenzo, et al.
Published: (2025)
by: De Martino, Vincenzo, et al.
Published: (2025)
Understanding Prompt Management in GitHub Repositories: A Call for Best Practices
by: Li, Hao, et al.
Published: (2025)
by: Li, Hao, et al.
Published: (2025)
What's Inside a GitHub Repository? An Empirical Study on the Contents of 10K Projects
by: Hora, Andre, et al.
Published: (2026)
by: Hora, Andre, et al.
Published: (2026)
Teaching Mining Software Repositories
by: Codabux, Zadia, et al.
Published: (2025)
by: Codabux, Zadia, et al.
Published: (2025)
An Empirical Study on the Effects of System Prompts in Instruction-Tuned Models for Code Generation
by: Cheng, Zaiyu, et al.
Published: (2026)
by: Cheng, Zaiyu, et al.
Published: (2026)
An Empirical Study of Self-Admitted Technical Debt in Machine Learning Software
by: Bhatia, Aaditya, et al.
Published: (2023)
by: Bhatia, Aaditya, et al.
Published: (2023)
On the Creation of Representative Samples of Software Repositories
by: Gorostidi, June, et al.
Published: (2024)
by: Gorostidi, June, et al.
Published: (2024)
Promptware Engineering: Software Engineering for Prompt-Enabled Systems
by: Chen, Zhenpeng, et al.
Published: (2025)
by: Chen, Zhenpeng, et al.
Published: (2025)
PromptDebt: A Comprehensive Study of Technical Debt Across LLM Projects
by: Aljohani, Ahmed, et al.
Published: (2025)
by: Aljohani, Ahmed, et al.
Published: (2025)
How do Software Engineering Researchers Use GitHub? An Empirical Study of Artifacts & Impact
by: Alrashedy, Kamel, et al.
Published: (2023)
by: Alrashedy, Kamel, et al.
Published: (2023)
Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
by: Shin, Jiho, et al.
Published: (2023)
by: Shin, Jiho, et al.
Published: (2023)
Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification
by: Binkhonain, Manal, et al.
Published: (2025)
by: Binkhonain, Manal, et al.
Published: (2025)
The Product Beyond the Model -- An Empirical Study of Repositories of Open-Source ML Products
by: Nahar, Nadia, et al.
Published: (2023)
by: Nahar, Nadia, et al.
Published: (2023)
An Empirical Study of Sustainability in Prompt-driven Test Script Generation Using Small Language Models
by: Kumari, Pragati, et al.
Published: (2026)
by: Kumari, Pragati, et al.
Published: (2026)
Automated Generation of Commit Messages in Software Repositories
by: Palakodeti, Varun Kumar, et al.
Published: (2025)
by: Palakodeti, Varun Kumar, et al.
Published: (2025)
Prompt-Enhanced Software Vulnerability Detection Using ChatGPT
by: Zhang, Chenyuan, et al.
Published: (2023)
by: Zhang, Chenyuan, et al.
Published: (2023)
Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance
by: Gesi, Jiri, et al.
Published: (2024)
by: Gesi, Jiri, et al.
Published: (2024)
An Empirical Study of Vulnerable Package Dependencies in LLM Repositories
by: Liu, Shuhan, et al.
Published: (2025)
by: Liu, Shuhan, et al.
Published: (2025)
Reliability of Large Language Models for Design Synthesis: An Empirical Study of Variance, Prompt Sensitivity, and Method Scaffolding
by: Iftikhar, Rabia, et al.
Published: (2026)
by: Iftikhar, Rabia, et al.
Published: (2026)
Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization
by: Midolo, Alessandro, et al.
Published: (2026)
by: Midolo, Alessandro, et al.
Published: (2026)
A Large-Scale Empirical Study of AI-Generated Code in Real-World Repositories
by: Mao, Tianhao, et al.
Published: (2026)
by: Mao, Tianhao, et al.
Published: (2026)
An Empirical Validation of Open Source Repository Stability Metrics
by: Adejumo, Elijah Kayode, et al.
Published: (2025)
by: Adejumo, Elijah Kayode, et al.
Published: (2025)
Analyzing Prompt Influence on Automated Method Generation: An Empirical Study with Copilot
by: Fagadau, Ionut Daniel, et al.
Published: (2024)
by: Fagadau, Ionut Daniel, et al.
Published: (2024)
An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts
by: Rzig, Dhia Elhaq, et al.
Published: (2025)
by: Rzig, Dhia Elhaq, et al.
Published: (2025)
Can GPT-4 Replicate Empirical Software Engineering Research?
by: Liang, Jenny T., et al.
Published: (2023)
by: Liang, Jenny T., et al.
Published: (2023)
Similar Items
-
An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?
by: Suh, Hyunjae, et al.
Published: (2024) -
Human or LLM? A Comparative Study on Accessible Code Generation Capability
by: Suh, Hyunjae, et al.
Published: (2025) -
Does Documentation Matter? An Empirical Study of Practitioners' Perspective on Open-Source Software Adoption
by: Imani, Aaron, et al.
Published: (2024) -
Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks
by: Santana Jr, E. G., et al.
Published: (2025) -
Context Conquers Parameters: Outperforming Proprietary LLM in Commit Message Generation
by: Imani, Aaron, et al.
Published: (2024)